---
title: "OpenAI APIs - Embedding"
metatags:
    description: "This tutorial covers the embedding APIs for embedding models."
---
SGLang provides OpenAI-compatible APIs to enable a smooth transition from OpenAI services to self-hosted local models.
A complete reference for the API is available in the [OpenAI API Reference](https://platform.openai.com/docs/guides/embeddings).

This tutorial covers the embedding APIs for embedding models. For a list of the supported models see the [corresponding overview page](../supported-models)



## Launch A Server

Launch the server in your terminal and wait for it to initialize. Native encoder embedding architectures and `google/embeddinggemma-300m` are detected automatically. Decoder-style embedding models still require `--is-embedding`.



```python Example
from sglang.test.doc_patch import launch_server_cmd
from sglang.utils import wait_for_server, print_highlight, terminate_process

embedding_process, port = launch_server_cmd(
    """
sglang serve --model-path Alibaba-NLP/gte-Qwen2-1.5B-instruct \
    --is-embedding --log-level warning
"""
)

wait_for_server(f"http://localhost:{port}")
```

## Using cURL



```python Example
import subprocess, json

text = "Once upon a time"

curl_text = f"""curl -s http://localhost:{port}/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{{"model": "Alibaba-NLP/gte-Qwen2-1.5B-instruct", "input": "{text}"}}'"""

result = subprocess.check_output(curl_text, shell=True)

print(result)

text_embedding = json.loads(result)["data"][0]["embedding"]

print_highlight(f"Text embedding (first 10): {text_embedding[:10]}")
```

## Using Python Requests



```python Example
import requests

text = "Once upon a time"

response = requests.post(
    f"http://localhost:{port}/v1/embeddings",
    json={"model": "Alibaba-NLP/gte-Qwen2-1.5B-instruct", "input": text},
)

text_embedding = response.json()["data"][0]["embedding"]

print_highlight(f"Text embedding (first 10): {text_embedding[:10]}")
```

## Using OpenAI Python Client



```python Example
import openai

client = openai.Client(base_url=f"http://127.0.0.1:{port}/v1", api_key="None")

# Text embedding example
response = client.embeddings.create(
    model="Alibaba-NLP/gte-Qwen2-1.5B-instruct",
    input=text,
)

embedding = response.data[0].embedding[:10]
print_highlight(f"Text embedding (first 10): {embedding}")
```

## Using Input IDs

SGLang also supports `input_ids` as input to get the embedding.



```python Example
import json
import os
from transformers import AutoTokenizer

os.environ["TOKENIZERS_PARALLELISM"] = "false"

tokenizer = AutoTokenizer.from_pretrained("Alibaba-NLP/gte-Qwen2-1.5B-instruct")
input_ids = tokenizer.encode(text)

curl_ids = f"""curl -s http://localhost:{port}/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{{"model": "Alibaba-NLP/gte-Qwen2-1.5B-instruct", "input": {json.dumps(input_ids)}}}'"""

input_ids_embedding = json.loads(subprocess.check_output(curl_ids, shell=True))["data"][
    0
]["embedding"]

print_highlight(f"Input IDs embedding (first 10): {input_ids_embedding[:10]}")
```

## Compact Base64 Responses

Set `encoding_format` to `base64` when JSON arrays would dominate response size. The encoded value contains little-endian FP32 values and can be decoded by OpenAI-compatible clients.

```python Example
response = requests.post(
    f"http://localhost:{port}/v1/embeddings",
    json={
        "model": "Alibaba-NLP/gte-Qwen2-1.5B-instruct",
        "input": text,
        "encoding_format": "base64",
    },
)

base64_embedding = response.json()["data"][0]["embedding"]
print_highlight(f"Base64 embedding: {base64_embedding[:20]}...")
```


```python Example
terminate_process(embedding_process)
```

## Multi-Modal Embedding Model
Please refer to [Multi-Modal Embedding Model](../supported-models)
